Obesity is a complex metabolic isorder that affects a significant percentage of the industrialized population and is a significant risk factor for various types of cancer, type 2 diabetes, dyslipidemia, hypertension and coronary artery disease. In the struggle to maintain an optimal body mass index (BMI), Americans also spend approximately $40 billion annually on weight-loss products such as diet foods and drinks, artificial sweeteners, books and weight loss programs. Twin, adoption and family studies indicate that 40-70% of inter-individual variation in body mass index is heritable. Recent genome-wide association studies (GWAS) sampling from the general population have identified multiple genetic variants associated with obesity risk but cumulatively explain only a small fraction of the inherited variability in BMI. A recurring problem in traditional GWAS analysis is the lack of power to detect small effects and lack of reproducibility of variant SNP association in replication studies or in different ethnic populations. The broad, long-term objective and the goal of the specific research proposed here is to address these gaps and establish methods that significantly advance understanding of the complex genetic architecture underlying obesity and weight-loss/weight-maintenance. This will be achieved through a genome-wide association study of carefully phenotyped individuals at the extremes of body mass (instead of sampling from the general population) and by the application of innovative secondary analyses that complement and augment traditional approaches. Specifically, three aims are proposed - (i) to identify variant SNPs associated with obesity, weight-loss and weight-maintenance;(ii) to identify genes and gene-sets (pathways) that associate with obesity and obesity-related outcomes and (iii) to identify predictive markers for sub-categories of weight-loss success and prevention of weight gain. Each specific aim will be achieved through a specific analytic method - (i) traditional, single SNP based association analysis of continuous and discrete traits for specific aim 1;(ii) exploratory, secondary analysis for identifying genes (combination of SNPs) and pathways (combination of genes) associated with disease outcomes using combinatorial, gene-set enrichment techniques for specific aim 2;(iii) predictive modeling for determining trajectories of weight-loss and weight-regain over time and identifying predictive biomarkers of weight-loss outcomes for specific aim 3. The primary analysis (specific aim 1) is required to enable secondary analyses in specific aims 2 and 3. These efforts will lead to a better understanding of specific obesity-related outcomes, inform the design and content of future studies, and identify predictive genetic biomarkers of long term weight-maintenance or prevention of weight gain that can guide future clinical practice in the treatment of weight-related disorders. All of these are directly relevant to the mission of NIDDK. PUBLIC HEALTH RELEVANCE: The proposed research, 'Secondary nalysis of a Large Scale Genetic Study in Obesity and Related Outcomes'is relevant to public health in two ways. First, the research explores innovative analytic solutions on a genome-wide association study on obesity, weight-loss success and long-term weight maintenance, for the identification of genes and biological pathways that underlie the genetic architecture of obesity and obesity related metabolic traits. These findings could help uncover disease targets for the subsequent development of pharmacologic agents for the treatment of obesity and also help identify genetic biomarkers that have clinical utility in the prediction of long-term weight maintenance or prevention of weight gain. Secondly, the analytic approaches developed and evaluated in this proposal are also applicable to other genome-wide association studies and as such, have far-reaching consequences in enhancing the power of large-scale human genetics to address major public health concerns.